Loss functions also play an important role in the segmentation task. Liang et al. [1]} improved their loss function with weighted focal loss that performed well in the MICCAI 15 dataset [2]}. Since the traditional Dice loss [3]} is unfavorable to small structures as a few voxels of misclassification can lead to a large decrease of the Dice score, and this sensitivity is irrelevant to the relative sizes among structures. Therefore, balancing by label frequencies is suboptimal for Dice loss. Exponential Logarithmic loss [4]}, combining Focal loss [5]} with Dice loss, eased this issue without much adjustments of the network. However, it will reduce the accuracy of hard voxels in large or other easy regions.
